WB Project Data Preprocessing

Author

Luisa M. Mimmi

Published

September 6, 2024

Work in progress

# Pckgs -------------------------------------
if (!require ("pacman")) (install.packages("pacman"))
p_load(tidyverse, 
       janitor, skimr,
       here, paint,
       readxl,
       repurrrsive, # examples of recursive lists
       listviewer, # provides an interactive method for viewing the structure of a list.
       httr, jsonlite, XML,xml2, 
       oai, # R client to work with OAI-PMH 
       citr,
       fs)

—————————————————————————-

Data sources

WB Projects & Operations

World Bank Projects & Operations: Data Catalog https://projects.worldbank.org/en/projects-operations/project-search

Accessibility Classification: public under Creative Commons Attribution 4.0
esempio https://datacatalog.worldbank.org/search/dataset/0037800 https://datacatalog.worldbank.org/search/dataset/0037800/World-Bank-Projects---Operations

Raw data

I retrieved manually ALL WB projects approved between FY 1973 and 2023 (last FY incomplete) on 09/22/2022 (WDRs go from 1978-2022) using this example url FY 1978/79 and saved individual .xlsx files in data/raw_data/projects

—————————————————————————

{Ingest Projects data (via API)}

DOESN’T WORK!

1/2 Ingest Projects data (manually split)

I retrieved manually ALL WB projects approved between FY 1973 and 2023 (last FY incomplete) on 09/22/2022 (WDRs go from 1978-2022) using this example url and saved individual .xlsx files in data/raw_data/project

note the manual download is limited to # = 500

— Load all `.xlsx` files separately

— Save objs in folder as `.Rds` files separately

2/2 Ingest Projects data (manually all together)

I retrieved manually ALL WB projects approved between FY 1973 and 2023 (last FY incomplete) on 31/08/2024 using simply the Excel button on this page this WBG Projects and saved HUUUGE .xlsx files in data/raw_data/project2/all_projects_as_of29ago2024.xls

all_projects_as_of29ago2024   <- read_excel("data/raw_data/project2/all_projects_as_of29ago2024.xls", 
                         col_names = FALSE,
                         skip = 1) 
# Nomi delle colonne
cnames <- read_excel("data/raw_data/project2/all_projects_as_of29ago2024.xls", 
                         col_names = FALSE,
                         skip = 1,
                     n_max = 2) 
# file completo
all_proj <- read_excel("data/raw_data/project2/all_projects_as_of29ago2024.xls", 
                         col_names = TRUE,
                         skip = 2) 

save(all_proj, file = "data/raw_data/project2/all_projects_as_of29ago2024.Rdata")

Explore Project mega file

paint::paint(cnames)
paint::paint(all_proj)
 
skimr::skim(all_proj$pdo)  # complete_rate 0.503
skimr::skim(all_proj$boardapprovaldate) # complete_rate 0.779
skimr::skim(all_proj$closingdate) # complete_rate 0.707

Clean all_proj

# Mess of data format weird in different ways in 2 cols: 
    # 1947-12-31 12:00:00 # closingdate
    # 8/3/1948 12:00:00 AM # closingdate
    # 
    # 1955-03-15T00:00:00Z # boardapprovaldate
 
# Mutate the date columns to parse the dates, handling different formats and blanks
all_proj_t <- all_proj %>%
   # 1) Parsed using parse_date_time() with mdy HMS and mdy HMSp to handle "MM/DD/YYYY HH:MM AM/PM" formats.
  mutate(across("closingdate", ~ if_else(
    . == "", 
    NA_POSIXct_,  # Return NA for blank entries
    parse_date_time(., orders = c("mdy HMS", "mdy HMSp"))
  )),
    # 2) Parsed using ymd_hms() because it follows the ISO 8601 format (e.g., "1952-04-29T00:00:00Z").
  across("boardapprovaldate", ~ if_else(
    . == "", 
    NA_POSIXct_,  # Return NA for blank entries
    ymd_hms(., tz = "UTC")  # Handle ISO 8601 format (e.g., "1952-04-29T00:00:00Z")
  ))) %>% 
   mutate(boardapproval_year = year(boardapprovaldate),
          boardapproval_month = month(boardapprovaldate)) %>% 
   mutate(boardapprovalFY = case_when( 
             boardapproval_month >= 1 & boardapproval_month < 7 ~ boardapproval_year,
             boardapproval_month >= 7 & boardapproval_month <= 12 ~ boardapproval_year +1)) %>% 
   relocate(boardapprovalFY, .after = boardapprovaldate ) %>% 
   mutate(closingdate_year = year(closingdate),
          closingdate_month = month(closingdate)) %>% 
   mutate(closingdateFY = case_when( 
             closingdate_month >= 1 & closingdate_month < 7 ~ closingdate_year,
             closingdate_month >= 7 & closingdate_month <= 12 ~ closingdate_year +1)) %>% 
   relocate(closingdateFY, .after = closingdate ) 

tabyl(all_proj$closingdate)   
tabyl(all_proj_t$closingdateFY)

tabyl(all_proj$boardapprovaldate)   
tabyl(all_proj_t$boardapprovalFY)

Explore who are the ones with no PDO

# Function to count missing values in a subset of columns
count_missing_values <- function(data, columns) {
  # Select the subset of columns
  df_subset <- data %>% select(all_of(columns))
  
  # Use skimr to skim the data
  skimmed <- skim(df_subset)
  
  # Extract the relevant columns for column names and missing values
  missing_table <- skimmed %>%
    select(skim_variable, n_missing)
  
  # Return the table
  return(missing_table)
}

# Use the function on a subset of columns
count_missing_values(all_proj_t, c("pdo", "projectstatusdisplay", "boardapprovalFY", "sector1", "theme1"))

missing_pdo <- all_proj_t %>% 
   #select(id, pdo, countryname, projectstatusdisplay, lendinginstr, boardapprovalFY, projectfinancialtype) %>% 
   filter(is.na(pdo))

tabyl(all_proj_t$projectstatusdisplay) %>%  adorn_pct_formatting()
tabyl(missing_pdo$projectstatusdisplay) %>%  adorn_pct_formatting()

tabyl(all_proj_t$regionname)  %>% adorn_pct_formatting() 
tabyl(missing_pdo$regionname)  %>% adorn_pct_formatting() 

tabyl(all_proj_t$boardapprovalFY) %>%  adorn_pct_formatting()
tabyl(missing_pdo$boardapprovalFY) %>%  adorn_pct_formatting()

tabyl(all_proj_t$projectfinancialtype)  %>% adorn_pct_formatting() 
tabyl(missing_pdo$projectfinancialtype)  %>% adorn_pct_formatting() 

tabyl(all_proj_t$sector1)  %>% adorn_pct_formatting() 
tabyl(missing_pdo$sector1)  %>% adorn_pct_formatting() 

tabyl(all_proj_t$theme1)  %>% adorn_pct_formatting() 
tabyl(missing_pdo$theme1)  %>% adorn_pct_formatting() # most NA

#Environmental Assessment Category
tabyl(all_proj_t$envassesmentcategorycode)  %>% adorn_pct_formatting() # most NA
tabyl(missing_pdo$envassesmentcategorycode)  %>% adorn_pct_formatting() 
# Environmental and Social Risk
tabyl(all_proj_t$esrc_ovrl_risk_rate)  %>% adorn_pct_formatting() # most NA
tabyl(missing_pdo$esrc_ovrl_risk_rate)  %>% adorn_pct_formatting() 

tabyl(all_proj_t$lendinginstr)  %>% adorn_pct_formatting()  # Specific Investment Loan 4928   43.9%
tabyl(missing_pdo$lendinginstr)  %>% adorn_pct_formatting()  # Specific Investment Loan 4928   43.9%

Based on some “critical” category, I would say that even if many projects are missing PDO the incidence seems to happen at random, except maybe for lendinginstr specific Investment Loan are missing PDO in 4928 pr (43.9%). Why?

>>>>>> QUI <<<<<<<<<<<<<<<<<<

REDUCED DATASET of PROJECTS

For my purposes it is safe to drop all the projects with missing PDO !

it turns out there are no Development objectives spelled out until FY2001

projs <- all_proj_t %>% 
   filter(!is.na(pdo)) %>% 
   filter(!is.na(projectstatusdisplay)) %>% 
   filter(boardapprovalFY < 2024 & boardapprovalFY >1972)


tabyl(projs$boardapprovalFY)  %>% adorn_pct_formatting() # most NA

—————————————————————————

World Development Reports (WRDs)

DATA https://datacatalog.worldbank.org/search/dataset/0037800
INSTRUCTIONS https://documents.worldbank.org/en/publication/documents-reports/api
Following: (Kaye 2019; Robinson 2017; Robinson and Silge 2022)

# Function(s) -------------------------------------
source(here::here ("R", "f_scrape_WB-OKR.R") )
source(here::here ("R", "turn_list_to_tibble.R") )

Raw data

OAI-PMH

OAI-PMH (Open Archives Initiative Protocol for Metadata Harvest-ing) services, a protocol developed by the Open Archives Initiative (https://en.wikipedia.org/wiki/Open_Archives_Initiative). OAI-PMH uses XML data format transported over HTTP.

packg: Package oai is built on xml2 and httr.
paging: OAI-PMH uses (optionally) resumptionTokens, with an optional expiration date. These tokens can be used to continue on to the next chunk of data, if the first request did not get to the end.

1. World Bank list of World Development Reports

Genreal OKR link https://openknowledge.worldbank.org/search?spc.page=1&query=%20&scope=3d9bbbf6-c007-5043-b655-04d8a1cfbfb2

https://openknowledge.worldbank.org/entities/publication/5e5ac9f1-71ee-4734-825e-60966658395f 2023 | key takeaways | https://openknowledge.worldbank.org/server/api/core/bitstreams/54de9b54-dc23-43da-9a88-fe94dd5a3c24/content

https://openknowledge.worldbank.org/server/api/core/bitstreams/e1e22749-80c3-50ea-b7e1-8bc332d0c2ff/content

World Development Report (WDR);
- (in 2022) https://openknowledge.worldbank.org/handle/10986/2124
- (in 2024) https://openknowledge.worldbank.org/collections/3d9bbbf6-c007-5043-b655-04d8a1cfbfb2?spc.sf=dc.date.issued&spc.sd=DESC
- (in 2024) https://openknowledge.worldbank.org/collections/3d9bbbf6-c007-5043-b655-04d8a1cfbfb2?spc.sf=dc.date.issued&spc.sd=DESC&f.supportedlanguage=en,equals&spc.page=1&spc.rpp=100

—————————————————————————

install.packages("rvest")
library(rvest)

link2023 <- "https://openknowledge.worldbank.org/entities/publication/5e5ac9f1-71ee-4734-825e-60966658395f/full"
WDR2023 <- read_html(link2023)

WDR2023  %>%
  html_elements(xpath = "")

1. World Bank list of World Development Reports

Here I want to extract documents metadata from WBG OKR repository.

Which metadata: + collections: “Books” https://openknowledge.worldbank.org/handle/10986/4 + sub-collections: “Corporate Flagships” https://openknowledge.worldbank.org/handle/10986/2123 + World Development Report (WDR); https://openknowledge.worldbank.org/handle/10986/2124 https://openknowledge.worldbank.org/collections/3d9bbbf6-c007-5043-b655-04d8a1cfbfb2?spc.sf=dc.date.issued&spc.sd=DESC

  + Global Economic Prospects (GEP), 
  + Doing Business (DB), and 
  + Poverty and Shared Prosperity (PSP).  
  + ...

I can search adding a keyword:
+ World Development Report (WDR); + Keyword = “economic development”
… the url would become: https://openknowledge.worldbank.org/handle/10986/2124/discover?filtertype=subject&filter_relational_operator=equals&filter=economic+development

>>>>>> QUI <<<<<<<<<<<<<<<<<<

API response

# DATA https://datacatalog.worldbank.org/search/dataset/0037800 
# INSTRUCTIONS https://documents.worldbank.org/en/publication/documents-reports/api

# VARIABLES ----------------------------------------------
# base url for query
base <- "https://openknowledge.worldbank.org/oai/request?verb=ListRecords&metadataPrefix=oai_dc&set=col_10986_"
# base url with resumption token
base0 <- "https://openknowledge.worldbank.org/oai/request?verb=ListRecords&resumptionToken=oai_dc///col_10986_"
 
n <- 45 
subid <-   2124 # WRD 

temp_url <- paste0(base, subid)

# response <- httr::GET(temp_url) # xml_document
# response <- httr::content(response, as="parsed")
response <- xml2::read_xml(x = temp_url, encoding = "",
                           options = "NOBLANKS") # remove blank nodes
class(response)
 
# Parse a url into its component pieces.
xml2::xml_name(response) # [1] "OAI-PMH"
xml2::xml_name(xml_children(response)) # [1] "responseDate" "request"  "ListRecords" 

# print the full path directory:
response %>% xml_find_all( '//*') %>% xml_path()

# check length of id
#======# DON'T KNOW HOW SHE NEW THE NAME #======# 
# "/*/*[3]/*[1]/*[2]/oai_dc:dc/dc:identifier[1]"  
r_id <-  xml2::xml_find_all(response, ".//d1:identifier") %>% xml_text()
l <- length(r_id)
print(paste("length of total list:", l))

# #======#  check length of subject ???  #======# 
# "/*/*[3]/*[1]/*[2]/oai_dc:dc/dc:subject[216]" 
r_subject <-  xml2::xml_find_all(response, ".//d1:subject") %>% xml_text() # empty 
r_subject <-  xml2::xml_find_all(response, ".//subject") %>% xml_text() # empty 


# START NAVIGATION
r_root <- xml2::xml_root(response )   # Returns root node
r_root
r_cdr <- xml2::xml_children(response )   # Access the children nodes
r_cdr
str(r_cdr)

# all records as a list of lists 
r_cdr_l <- xml2::as_list(r_cdr)
str(r_cdr_l)
# only the info in record (l1-of - l45)
r_cdr_l_records <-  r_cdr_l[3]

# only records as a list of 45 
records <-  r_cdr_l_records[[1]] 
records[[1]][["metadata"]][["dc"]][["subject[1]"]]


# name the records 
names(records) <- paste0 ("WRD_", 1:length(records))
records_names <- c(names(records))
class(records_names)

Parsing xml ONE INDICATOR AT A TIME (header)

… (form header) identifier

# # # identifier of record 1 
# # records[["WRD_1"]][["header"]][["identifier"]]
# # # identifier of record 2 
# # records[["WRD_2"]][["header"]][["identifier"]]
# 
# # all identifier 
# 
# # 1) Prepare empty vectors 
# id_l <- vector(mode = "character", length = length(records))
# id_v <- vector(mode = "character", length = length(records))
# # 2) 
# for(i in seq_along(records)) {
#  # extract ALL identifier LIST LIST 
#   id_l[[i]] <- records[[i]][["header"]][["identifier"]]
#   # transform list in vector
#    id_v[[i]] <- unlist(id_l[[i]])
# }
# 
# # 3) 
# id_v_t <- tibble::as_tibble_col(id_v, column_name ="WDR_hd_id")
# df_header_identifier <- id_v_t

… (form header) setSpec

# # 0) set variables 
# meta_item <- "setSpec"
# l <- records
# 
# # 1) Prepare empty output 
# item_l <- list(mode = "character", length = length(l))
# item_v <- vector(mode = "character", length = length(l))
# 
# # 2) ET loop 
# for(i in seq_along(l)) {
#  # extract ALL setSpec LIST LIST 
#   item_l[[i]] <- l[[i]][["header"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WDR_hd_", meta_item)
# 
# # 4) make it a tibble   
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# 
# # 5) rename result df 
# # assign("new.name",old.name)
# assign(paste0("df_header_", meta_item),item_v_t)
# 
# df_header_setSpec <- item_v_t # non so se serve a qlc

Parsing xml ONE INDICATOR AT A TIME (metadata)

here I am going to list one level up records[["WRD_1"]][["metadata"]][["dc"]][["item]]
Problem: multiple repetition of some “items”

… (form metadata) title

# # EG all title(s)
# # records[["WRD_1"]][["metadata"]][["dc"]][["title"]]
# 
# # 1) Prepare empty ouppt 
# item_l <- list(mode = "character", length = length(records))
# item_v <- vector(mode = "character", length = length(records))
# # 2) 
# for(i in seq_along(records)) {
#  # extract ALL identifier LIST LIST 
#   item_l[[i]] <- records[[i]][["metadata"]][["dc"]][["title"]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# column_name <- paste0("WRD_mt_", "title")  
# 
# # 3) 
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# df_meta_title <- item_v_t

… (form metadata) creator

# # EG all creator(s)
# # records[["WRD_1"]][["metadata"]][["dc"]][["creator"]]
# 
# # 0) choose metat_item
# meta_item <- "creator"
# 
# # 1) Prepare empty ouppt 
# item_l <- list(mode = "character", length = length(records))
# item_v <- vector(mode = "character", length = length(records))
# # 2) 
# for(i in seq_along(records)) {
#  # extract ALL identifier LIST LIST 
#   item_l[[i]] <- records[[i]][["metadata"]][["dc"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WRD_mt_", meta_item)
# 
# # 3) 
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# df_meta_creator<- item_v_t

… (form metadata) identifier

#  # 0) set variables 
# meta_item <- "identifier"
# l <- records
# 
# # 1) Prepare empty output 
# item_l <- list(mode = "character", length = length(l))
# item_l
# item_v <- vector(mode = "character", length = length(l))
# item_v
# # 2) ET loop 
# for(i in seq_along(l)) {
#  # extract ALL identifier LIST LIST 
#   item_l[[i]] <- l[[i]][["metadata"]][["dc"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WRD_mt_", meta_item)
# 
# # 4) make it a tibble   
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# 
# # 5) rename result df 
# # assign("new.name",old.name)
# # assign(paste0("df_meta_", meta_item),item_v_t)
# df_meta_identifier <- item_v_t

… (form metadata) description

#  # 0) set variables 
# meta_item <- "description"
# l <- records
# 
# # 1) Prepare empty output 
# item_l <- list(mode = "character", length = length(l))
# item_l
# item_v <- vector(mode = "character", length = length(l))
# item_v
# # 2) ET loop 
# for(i in seq_along(l)) {
#  # extract ALL description LIST LIST 
#   item_l[[i]] <- l[[i]][["metadata"]][["dc"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WRD_mt_", meta_item)
# 
# # 4) make it a tibble   
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# 
# # 5) rename result df 
# # assign("new.name",old.name)
# assign(paste0("df_meta_", meta_item),item_v_t)

… (form metadata) date

#  # 0) set variables 
# meta_item <- "date"
# l <- records
# 
# # 1) Prepare empty output 
# item_l <- list(mode = "character", length = length(l))
# item_l
# item_v <- vector(mode = "character", length = length(l))
# item_v
# # 2) ET loop 
# for(i in seq_along(l)) {
#  # extract ALL date LIST LIST 
#   item_l[[i]] <- l[[i]][["metadata"]][["dc"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WRD_mt_", meta_item)
# 
# # 4) make it a tibble   
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# 
# # 5) rename result df 
# # assign("new.name",old.name)
# assign(paste0("df_meta_", meta_item),item_v_t)

… (form metadata) subject

#  # 0) set variables 
# meta_item <- "subject"
# l <- records
# 
# # 1) Prepare empty output 
# item_l <- list(mode = "character", length = length(l))
# item_l
# item_v <- vector(mode = "character", length = length(l))
# item_v
# # 2) ET loop 
# for(i in seq_along(l)) {
#  # extract ALL subject LIST LIST 
#   item_l[[i]] <- l[[i]][["metadata"]][["dc"]][[meta_item]]
#   # transform list in vector
#    item_v[[i]] <- unlist(item_l[[i]])
# }
# # 3) set column name 
# column_name <- paste0("WRD_mt_", meta_item)
# 
# # 4) make it a tibble   
# item_v_t <- as_tibble_col(item_v, column_name =column_name)
# 
# # 5) rename result df 
# # assign("new.name",old.name)
# assign(paste0("df_meta_", meta_item),item_v_t)

same but as FUNCTION -> YEP!!!!!!!

####… ALL from header (func)

# ---------- FUNCTION to create a column of Titles --> DOES NOT Work! 
# source(here::here ("R", "turn_list_to_tibble.R") )

# ------------- USE FUNCTION  (called INSIDE THE `assign`)
## 1) input: header --> identifier
list <- records
item_name <- "identifier"
where <- "header" # or "header"

df_name <- paste("col", item_name, sep = "_")

## function call (that changes the name of output on the fly )
assign(df_name, value = turn_list_to_tibble(list, where, item_name ))

## 2) input: header --> setSpec
list <- records
item_name <- "setSpec"
where <- "header" # or "header"

df_name <- paste("col", item_name, sep = "_")

## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col", item_name, sep = "_")
assign(df_name, value = df)

####… ALL from meta (func)

## ------- --------- 1) input: meta --> identifier
list <- records
item_name <- "identifier"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

## --------- 2) input: meta --> title
list <- records
item_name <- "title"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

## --------- 3) input: meta --> date
list <- records
item_name <- "date"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

## --------- 4) input: meta --> creator
list <- records
item_name <- "creator"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

## --------- 5) input: meta --> subject
list <- records
item_name <- "subject"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

## --------- 6)  input: meta --> description
list <- records
item_name <- "description"
where <- "meta" # or "meta"
## function call (that changes the name of output on the fly )
df<- turn_list_to_tibble(list, where, item_name )
# rename ouptut
df_name <- paste("col_m", item_name, sep = "_")
assign(df_name, value = df)

… bind ALL - ALL cols into 1 tibble

list_col  <-  ls(pattern =  '^col_', all.names = TRUE)
list_col
 cat(noquote(list_col) )
df_metadata <- bind_cols(col_identifier,
                          col_m_identifier,
                          col_m_title,
                          col_m_date,
                          col_m_creator,
                          col_m_subject,
                          col_m_description,
                          col_setSpec, .name_repair = "unique"
                          ) %>% 
  janitor::clean_names()

save in data/derived_data

names(df_metadata)
dataDir <- fs::path_abs(here::here("data","raw_data"))
fileName <- "/WDR.rds"
Dir_File <- paste0(dataDir, fileName)
write_rds(x = df_metadata, file = Dir_File)

Subject / keywords problem

Following SO Rsponse

—- DONT RUN —–

xml_find_all(doc, ".//ArchivedIncident") %>% # iterate over each incident
  map_df(~{
    set_names(
      xml_find_all(.x, ".//value/value") %>% xml_text(), # get entry values
      xml_find_all(.x, ".//key") %>% xml_text()          # get entry keys (column names)
    ) %>% 
      as.list() %>%                                      # turn named vector to list
      flatten_df() %>%                                   # and list to df
      mutate(ID = xml_attr(.x, "ID"))                    # add id
  }) %>%
  type_convert() %>% # let R convert the values for you
  select(ID, everything()) # get it in the order you likely want
## # A tibble: 2 x 5
##      ID TEST1 TEST2 TEST3 TEST4
##   <int> <chr> <int> <chr> <chr>
## 1   100  <NA>    12     A  <NA>
## 2   101  BLAH    NA  <NA>  <NA>

# ---- my try 
child <-  xml_child(response, 3) 

xml_find_all(child, ".//record") %>% # iterate over each incident
  map_df(~{
    set_names(
      xml_find_all(.x, ".//subject/subject") %>% xml_text(), # get entry values
      xml_find_all(.x, ".//metadata") %>% xml_text()          # get entry keys (column names)
    ) %>% 
      as.list() %>%                                      # turn named vector to list
      flatten_df() %>%                                   # and list to df
      mutate(ID = xml_attr(.x, "ID"))                    # add id
  }) %>%
  type_convert() %>% # let R convert the values for you
  select(ID, everything()) # get it in the order you likely want

Here I see keywords

2022: https://openknowledge.worldbank.org/handle/10986/36883?show=full

oai:openknowledge.worldbank.org:10986/2586

# read WRD metadata
WDR <- readr::read_rds(here::here("data", "raw_data", "WDR.rds" ))


WDR <- WDR  %>% 
   # Extract only the portion of string AFTER the backslash {/}
   mutate(id =  stringr::str_extract(doc_mt_identifier_1, "[^/]+$") ) %>% 
   dplyr::relocate(id, .before = doc_mt_identifier_1) %>% 
   mutate(url_keys = paste0("https://openknowledge.worldbank.org/handle/10986/", id , "?show=full") )%>% 
   dplyr::relocate(url_keys, .before =  doc_mt_identifier_1)

WDR$id[1] 
WDR$url_keys[1] 
  
WDR$id[45] 
   
for (i in 1:45){
     print (WDR$url_keys[i] )
   }

REVIEW —

Go back and try to extract dates & keywords

REVIEW —

1. World Bank Projects & Operations:

—- DONT RUN —–

Wrangling the text

Following: (Kaye 2019; Robinson 2017)

Tokenization

A token is a meaningful unit of text, such as a word, that we are interested in using for analysis
bigrams

connections

Following the example of David Robinson on HN titles

Data sources:

World Bank Projects & Operations: https://datacatalog.worldbank.org/search/dataset/0037800 https://datacatalog.worldbank.org/search/dataset/0037800/World-Bank-Projects—Operations

Accessibility Classification: public under Creative Commons Attribution 4.0

World Bank - World Development Reports

Accessibility Classification:

Acknowledgements

Computing for Social Science Course
Stephanie Tran project who created the function R/f_scrape_WB-OKR.R
Renu Khandelwal tutorial

—————————————————————————-

Reference Tutorials

(Robinson and Silge 2022) (LDAL 2022) (edureka?!2019)

David Robinson on HN titles

Benjamin Soltoff: Computing 4 Social Sciences - API

Benjamin Soltoff: Computing 4 Social Sciences - text analysis

Ben Schmidt Book Humanities Crurse Ben Schmidt Book Humanities

tidyTuesday cast on tidytext

✔️ MEDIUM articles: common words, pairwise correlations - 2018-12-04
TidyTuesday Tweets - 2019-01-07
Wine Ratings - 2019-05-31 Lasso regression | sentiment lexicon,
Simpsons Guest Stars 2019-08-30 geom_histogram
Horror Movies 2019-10-22 explaining glmnet package | Lasso regression
The Office 2020-03-16 geom_text_repel from ggrepel | glmnet package to run a cross-validated LASSO regression
Animal Crossing 2020-05-05 Using geom_line and geom_point to graph ratings over time | geom_text to visualize what words are associated with positive/negative reviews |topic modelling

References

Kaye, Ella. 2019. “ELLA KAYE: Working with Text in R,” October. https://ellakaye.rbind.io/talks/2019-10-05-working-with-text-in-r/.

LDAL. 2022. “Tutorials.” https://ladal.edu.au/tutorials.html#5_Text_Analytics.

Robinson, David. 2017. “Words Growing or Shrinking in Hacker News Titles: A Tidy Analysis.” Variance Explained. June 8, 2017. http://varianceexplained.org/r/hn-trends/.

Robinson, David, and Julia Silge. 2022. [1] Welcome to Text Mining with R Text Mining with R. https://www.tidytextmining.com/.

—————————————————————————-

Data sources

WB Projects & Operations

Raw data

—————————————————————————

{Ingest Projects data (via API)}

1/2 Ingest Projects data (manually split)

— Load all .xlsx files separately

— Save objs in folder as .Rds files separately

2/2 Ingest Projects data (manually all together)

Explore Project mega file

Clean all_proj

Explore who are the ones with no PDO

>>>>>> QUI <<<<<<<<<<<<<<<<<<

REDUCED DATASET of PROJECTS

—————————————————————————

World Development Reports (WRDs)

Raw data

OAI-PMH

1. World Bank list of World Development Reports

—————————————————————————

1. World Bank list of World Development Reports

>>>>>> QUI <<<<<<<<<<<<<<<<<<

API response

Parsing xml ONE INDICATOR AT A TIME (header)

… (form header) identifier

… (form header) setSpec

Parsing xml ONE INDICATOR AT A TIME (metadata)

… (form metadata) title

… (form metadata) creator

… (form metadata) identifier

… (form metadata) description

… (form metadata) date

… (form metadata) subject

same but as FUNCTION -> YEP!!!!!!!

… bind ALL - ALL cols into 1 tibble

save in data/derived_data

Subject / keywords problem

Here I see keywords

REVIEW —

Go back and try to extract dates & keywords

REVIEW —

1. World Bank Projects & Operations:

Wrangling the text

Tokenization

connections

Data sources:

Acknowledgements

—————————————————————————-

Reference Tutorials

References

— Load all `.xlsx` files separately

— Save objs in folder as `.Rds` files separately